Bitcoin datetime analysis

This file contains the stock information of Bitcoin in an hourly basis for Jan 2022 and Feb 2022 from 01/01/2022 to 03/01/2022. Reference dataset from the kaggle.

#libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

import plotly.express as px
import plotly.graph_objects as go
import numpy as np

%matplotlib inline

#input file
INPUT_FILE_SERIES = "Bitcoin.csv"
series_df = pd.read_csv(INPUT_FILE_SERIES, parse_dates=True, index_col=0)
series_df2 = pd.read_csv(INPUT_FILE_SERIES)

Function to plot the date vs high values of bitcoin

# The function takes the start date, the no of occurence and the frequency as input
def plot_dateXhigh(startdate, period, frequency):
    # create a dataframe with the date_range function
    range_df = pd.date_range(startdate, periods=period, freq=frequency)
    # create a dataframe by locating the date_range against the series_df
    range_df = series_df.loc[range_df]
    # Find the date against the highest rate
    xmax = range_df["high"].idxmax()
    # Find the highest rate
    ymax = range_df["high"].max()
    # Create y axis range
    y_axis_low = range_df["high"].min() - 200
    y_axis_high = ymax + 200
    # **************************************************
    # Create plot
    plt.figure(figsize=(20,12))
    plt.plot(range_df["high"])
    # Mark the highest value in the plot
    plt.plot(xmax, ymax,"ro")
    graph_text = "Maximum Value = " + str(ymax)
    plt.text(xmax, ymax+50, graph_text, fontsize=16)
    plt.ylim(y_axis_low,y_axis_high)
    plt.xlabel("Time", fontsize=16)
    plt.ylabel("High value", fontsize=16)
    plt.show()

Hourly Analysis

# Plot the highest BTC value on Jan1 2022 hourly for 24 records
plot_dateXhigh("2022-01-01 00:00:00", 24, "1H")

# Plot the highest BTC value from Jan1 2022 daily for 31 records
plot_dateXhigh("2022-01-01 00:00:00", 31, "D")

Weekend Analysis

# Plot the highest BTC value from Jan1 2022 sunday for 5 records
plot_dateXhigh("2022-01-01 00:00:00", 5, "W-SUN")

series_df2['date']=pd.to_datetime(series_df2['date'])

series_df2['date'][0]

Timestamp('2022-03-01 03:43:00')

#close value of the bitcoin on any single day
plt.figure(figsize=(20,12))
plt.plot(series_df2['date'],series_df2['close'])
plt.title('Bitcoin')

Text(0.5, 1.0, 'Bitcoin')

low-high analysis

#calculating mean value 
series_df2["Mean Low-High"] = (series_df2["high"]+series_df2["low"])/2

fig = px.line(series_df2, x="date", y=["high","Mean Low-High","low"], log_y=True)

fig.update_layout(title=dict(text="Low-High",font=dict(size=22)))
fig.show()

As we can do analysis from the plot on Jan 24 2022 Bitcoin could be traded with the highest value of at 38.05k at 11:10 AM and lowest value of 32.96k at 11:06 AM at

Open-Close Analysis

fig = px.line(series_df2, x=series_df2.date, y=["open","close"], log_y=True)

fig.update_layout(title=dict(text="Open-Close ",font=dict(size=22)))

fig.show()

Volume analysis

fig = px.line(series_df2 , x="date", y="Volume USD", title='Volume Analysis')
fig.show()

The plot illustrated the number of bitcoins traded on any single day whereas Feb 24 2022 reached the peak about it with over 8M

fig = px.line(series_df2 , x="date", y="Volume BTC")
fig.show()

Analysing daily price change

series_df2['1day % return']=((series_df2['close']-series_df2['open'])/series_df2['close'])*100
series_df2.head()

	date	symbol	open	high	low	close	Volume BTC	Volume USD	Mean Low-High	1day % return
0	2022-03-01 03:43:00	BTC/USD	43046.58	43046.58	43046.58	43046.58	0.000000	0.000000	43046.580	0.000000
1	2022-03-01 03:41:00	BTC/USD	43018.23	43046.59	43018.23	43046.58	0.142977	6154.673021	43032.410	0.065859
2	2022-03-01 03:40:00	BTC/USD	43022.24	43022.24	43016.03	43016.03	0.009230	397.037957	43019.135	-0.014436
3	2022-03-01 03:39:00	BTC/USD	43035.16	43035.16	42999.44	42999.44	0.820950	35300.390270	43017.300	-0.083071
4	2022-03-01 03:38:00	BTC/USD	43077.82	43077.82	43049.46	43049.46	0.022210	956.143143	43063.640	-0.065878

Calculating and plotting %return at any single secondon and any single day

fig = px.line(series_df2, x="date", y="1day % return", title='Returns')
fig.show()

sns.pairplot(data=series_df2)

<seaborn.axisgrid.PairGrid at 0x19edc1a7d00>

series_df3=series_df2.copy()
series_df3['date']=pd.to_datetime(series_df3['date'])
series_df3.set_index('date',inplace=True)

series_df3['close'].resample('M').mean().plot()

<AxesSubplot:xlabel='date'>